Comparative Analysis of Financial Optimization Scenarios for PV and
Battery Storage Integration at Heraklion Port
EMMANUEL KARAPIDAKIS1, MARIOS NIKOLOGIANNIS2, MARINI MARKAKI1,
IOANNIS GRAMMATIKAKIS3
1Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion, GREECE
2Institute of Energy, Environment and Climate Change Hellenic Mediterranean University Heraklion, GREECE
3Unitech Ellas Heraklion, GREECE
Abstract: — The rapid expansion of renewable energy, driven by reduced installation costs, technological
advancements, and political support, necessitates efficient integration strategies. This study presents a comparative
analysis of financial optimization scenarios for the integration of photovoltaic (PV) systems and battery storage at
Heraklion Port. By evaluating multiple strategies, the research addresses the economic viability, cost-benefit ratios,
and payback periods of different configurations. This analysis considers the broader context of increasing pressure
on electricity grids and the need for sustainable solutions to manage dispersed renewable production and energy
offsetting. The findings aim to provide insights into optimal investment strategies that balance financial performance
with energy efficiency, thereby mitigating costs passed on to consumers and supporting the goals of energy
transition and sustainability in port infrastructure.
Key-words: —Renewable Energy Sources, Energy Storage, Ports, Energy investment, Sustainability
Received: March 12, 2024. Revised: August 21, 2024. Accepted: September 13, 2024. Published: October 9, 2024.
1. Introduction
In the quest for sustainability and resilience, large medium
voltage consumers are increasingly pursuing self-sufficiency
in energy production. With the rising demand for electricity
and mounting concerns about climate change, the integration
of electricity storage systems and photovoltaic (PV)
generation is emerging as a pivotal solution [1]. This
approach not only addresses the need for reliable power
supply but also contributes to reducing the carbon footprint
associated with traditional energy sources. The adoption of
PV and storage technologies enables consumers to manage
energy more efficiently, mitigate grid dependency, and
enhance overall energy security [1].
Traditional energy grids, while reliable, face challenges
related to sustainability, intermittent operation and
centralized control. Large installations , with their substantial
energy demands, significantly exacerbate these challenges.
Additionally, , their reliance on conventional energy sources
exposes them to price volatility and supply uncertainties [3].
Consequently, the urgent need for self-sufficiency arises from
both environmental and economic considerations. This shift
towards self-sufficiency not only aims to enhance
sustainability but also to stabilize energy costs and ensure a
more resilient energy supply[4].
Photovoltaic technology presents a reliable solution in this
context . By capturing solar energy and converting it into
electricity, photovoltaic systems provide a renewable and
abundant energy source [5]. Large consumer-owned
complexes, with their extensive rooftops and available land,
are well suited for harnessing solar energy [5]. However, the
integration of photovoltaics systems alone is insufficient to
effectively meet the diverse and complex energy demands of
these facilities.
Storage solutions, such as advanced battery technologies,
enable the capture and conservation of excess energy
produced by PV systems [6]. By storing surplus energy during
peak generation periods, large-scale PV arrays can mitigate
the intermittency of solar energy and ensure a continuous
supply even during periods of low or no sunlight [7].
Despite its promise, the path to self-sufficiency is fraught
with challenges. Complex techniques, including system
integration and optimization, require careful planning and
execution. Furthermore, regulatory frameworks and economic
barriers often hinder the adoption of renewable energy
solutions [8]. Overcoming these obstacles requires a holistic
approach that considers the specific technological and
application needs on a case-by-case basis.
The synergy between electricity storage and photovoltaics
has transformational potential for large energy-intensive
complexes [10]. Beyond achieving energy independence, the
integration enhances resilience, reduce costs, and improves
environmental management[11]. By embracing renewable
energy and decentralized power generation, these complexes
can pave the way to a more sustainable and secure energy
future.
2. Materials and Methods
2.1 Electricity Billing Scheme
In this study, the port of Heraklion is analyzed as a large
medium voltage consumer.
The consumption period considered is the year 2022. The
total consumption was 2307MWh with a maximum peak
power of 581.17kW.
The analysis is based on hourly load demand data for the
Port of Heraklion in the year 2022 (Fig. 1). The peak value
occurred during the morning hour on 30/10/2022 at 4:00 am,
with a power demand of 581.17kW, while the average
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
159
Volume 6, 2024
demand value is 263.75kW. However, it is noted that the
maximum value depicted in the figure deviates sugnificantly
from the typical demand pattern during those hours and will
not be considered.Excluding this outlier, the maximum value
occured on 19/8/2022 at 22:00 with a demand of 469.59kW.
Fig. 1. Hourly time series of cargo demand Port of Heraklion 2022.
Fig. 2. Duration curve of cargo demand Port of Heraklion 2022.
According to the load demand duration curve (Fig. 2),
approximately 53% of the annual demand falls within the
range of 270-400kW. It is notable (Fig. 3) that there is a
significant increase in consumption during the summer
months,particularly in August reaching 235.88MWh
attributrd to the tourist season and holiday periods such as
Christmas and Orthodox Easter . Conversely, the months with
the lowest consumption are April and November registering
160.02MWh and 161.40MWh, respectively.
Fig. 3. Monthly energy consumption for the Port of Heraklion.
Throughout the day, peak demand periods coincide with
increased power charges. Over the course of the year, it is
observed that demand rises from the afternoon and remains at
peak levels until the early morning hours. The peak in demand
notably occurs at 22:00.
Fig. 4. Energy consumption for each hour of the day.
As per decision of the Regulatory Authority for Energy, as
outlined in Government Gazette 198/2023, charges for the
utilization of the Greek Electricity Distribution Network
(EDNIE) are established. Addiotnally, this decision also
specifies the peak hours for each period. According to the
regulations, , during the winter months (January, February,
November and December), a total 7 hours per day are
designated as peak hours, spanning from 10:00 to 14:00 and
from 18:00 to 21:00.
During the spring months (March, April, May) peak hours
are designated at 4 per day spanning from 10:00 to 14:00. The
third peak load period occurs during the summer months
(June, July, August), comprising 6 hours per day starting at
11:00 and concluding at 17:00 .
During the autumn months of September and October, the
fourth peak period is defined, encompassing 7 hours per day
divided into two zones. The first zone extends from 10:00 to
14:00, while the second zone spans from 18:00-21:00.
2.2 Methodology
The comparison of hourly demand data, as previously
analyzed, with corresponding hourly data of electricity
production from photovoltaic power plants in a geographical
area near Heraklion port, reveals a relative similarity in terms
of summer seasonality (Fig. 5). As demand increases,
photovoltaic production alo rises. However, this correlation
does not hold throughout the day. During midday hours, when
the photovoltaic production peaks, consumption decreases at
the Heraklion port.
Fig. 5. Timing of summer seasonality of consumption – production.
The asynchrony between production – and demand
throughout the day underscores the necessity for energy
storage. Another contributing factor for energy storage
adoption is the surge in demand during peak periods, notably
in the winter and autumn seasons This surge results in
escalated charges, which are directly tied to the maximum
and average power demand during peak hours.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
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To implement the algorithm alongside hourly demand data
throughout the year, corresponding hourly electricity
production from photovoltaic panels is essential. Utilizing
actual hourly values from the PV plant, the operational status
of thesystem employing battery power is computed.
Hourly demand values are represented as Load[t] where
t=1,2,...,8760. Initially, the system is presumed to be fully
charged . Losses incurred during charging and discharging,
equivalnet to 2% are accounted for. The maximum charging
and discharging power are capped at 100kW, with a depth of
discharge (DOD) of up to 80%.
Fig. 6. Calculation per hour of system operation and energy input from the
grid.
2.3 PV-Battery Sizing
Given the significant expense associated with electricity
storage systems, sizing a system to store all the energy
required by the installation during periods of insufficient PV
output is not economically viable.. Similarly, installing a
system capable of producing annually as much energy as
consumed in the facilities at Heraklion Port is not feasible, as
surplus energy cannot be stored during peak photovoltaic
production hours.
Executing the algorithm in Fig. 6, various combinations of
installed PV panel power sizes with differing batterycapacities
were examined .
The PV utilization rate is defined as follows:
 

 󰇟󰇠 󰇟󰇠


󰇟󰇠


(1)
Where Dir[t] represents the annual energy produced by PV
and consumed directly, Bat_out[t] denote the annual battery
discharge energy fullfilling installation demand,and
PV_out[t] signifies the annual production of PV.
The PV utilization rate escalates as the installed power
decreases and the battery size increases (refer to Table 1).
This phenomenon arises due to the low demand during
midday hours, resulting in a diminished absorption rate of the
generated energy. Conversely, with larger battery capacities,
the absorbed energy diminishes.
TABLE I. PHOTOVOLTAIC EXPLOITATION RATE
PV \ Battery
200kWh
300kWh
400kWh
400kWp
88,45%
90,42%
92,12%
500kWp
78,90%
81,25%
83,43%
600kWp
69,67%
72,02%
74,22%
700kWp
61,96%
64,09%
66,13%
Simultaneously, the battery utilization rate was computed,
as depicted in Table II.
Battery utilization rate is defined:

󰇟󰇠

 
 
(2)
Where BAT_cap represents the capacity of the battery
system.
Battery utilization improves as the installed capacity of the
PV system increases (refer to Table II), as it results in a greater
surplus of energy available for storage. Conversely, reducing
the capacity of the battery system leads to improved
utilization, as less surplus energy is needed to charge them.
TABLE II. BATTERY EXPLOITATION RATE
PV \ Battery
200kWh
300kWh
400kWh
400kWp
60,17%
55,39%
51,37%
500kWp
79,73%
75,90%
72,74%
600kWp
88,82%
86,58%
84,08%
700kWp
92,43%
90,54%
88,58%
Table I illustrates that for an installed photovoltaic
capacity of 700kWp, the utilisation rate is less than 60%. Due
to the low integration rate of these systems, they will not be
considered in the economic analysis.
Additionally, during the winter and autumn months, PV
production ceases during the second zone of peak hours. To
mitigate power charges, which are computed during peak
hours, the algorithm for injectingstored energy can be adjusted
to inject energy exclusively during these peak periods.
Fig. 7. Optimized stored energy injection algorithm.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
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Volume 6, 2024
3. Results
In the winter months (Fig. 8), as well as in the autumn
months (Fig. 9), delaying the injection of stored power leads
to a reduction in the power demanded from the grid after
18:00, coinciding with the onset of the second peak zone,
which extends until 21:00. Particularly during the winter
months, the optimized algorithm appears t to offer prolonged
benefits. Subsequent the economic analyses will present the
outcomes of both algorithms.
Fig. 8. Battery optimisation in the winter months.
Fig. 9. Battery optimisation in the autumn months.
3.1 Economic Feasibility
The economic evaluation employs the criteria of Net
Present Value (NPV) and Internal Rate of Return (IRR). The
annual discount rate is set at 6%, considering installation costs
along with operation and maintenance expenses. For the
installation of PV panels, a cost of 800,00€/kWp is assumed
with operational and maintenance (O&M) costs of 7€/kWp.
For the installation of the lithium batteries, a cost of 350€/kWh
is considered , with maintenance costs of 4€/kWh.
The current average cost of energy is 0.14€/kWh. The
prevailing power consist of 0.987€/kW for average peak
power from suppliers, 6.66€/kW for maximum power at peak
hours, 3.24€/kW for average peak power for the transmission
system, and 42.826€/KVA/year for average active power at
peak hours as a unit fixed charge.
Based on the existing legislation on energy charges,
monthly bills were computed for the new consumptions. The
annual benefit derived from the optimised algorithm (Table
IV) exceeds that fom the conventional method (Table III) in
every combination.
TABLE III. ECONOMIC BENEFIT FOR THE CURRENT ENERGY PRICE
(0,14€) WITHOUT THE OPTIMIZED ALGORITHM
PV \ Battery
200kWh
300kWh
400kWh
400kWp
121,700.
42 €
124,132.
14 €
126,205.20
€
500kWp
135,448.
59 €
139,066.
71 €
142,401.41
€
600kWp
143,699.
24 €
148,130.
89 €
152,273.25
€
TABLE IV. ECONOMIC BENEFIT FOR THE CURRENT ENERGY PRICE
(0,14€) WITH THE OPTIMIZED ALGORITHM
PV \ Battery
200kWh
300kWh
400kWh
400kWp
122,203.
20 €
124,711.
18 €
126,763.57
€
500kWp
136,055.
41 €
139,835.
84 €
143,140.23
€
600kWp
144,245.
83 €
149,170.
94 €
153,191.62
€
The financial disparity resulting from the algorithm
considering peak hours during the winter and autumn months
can reach up to 1.040,05 €. This amount corresponds to a
7.55% increase in added value provided by the storage
system. Consequentlythe subsequent economic analysis will
focus on systems employing the optimized algorithm.
The internal efficiencies across the current energy price
range vary from 23.69% to 33.14% (see Fig. 10).
Fig. 10. Internal efficiency for energy charge 0,14€.
Similarly, the net present values range from from
682.604€ to 763.167€ (Fig 11).
Fig. 11. Net present value for energy charge 0,14€.
Upon comparing both criteria, systems with an installed
PV capacity of 500kWp and 600kWp emerge as superior
investments. Specifically, for a 500kWp system, the optimal
matched battery capacity is 200kWh, while for a 600kWp PV
system, a battery with a capacity of 300kWh appears
preferable.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
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3.2 Sensitivity Analysis
Admist a period of fluctuating energy costs, the impact of
reducing energy costs to 0.12€/kWh and increasing them to
0.16€/kWh is examined.
During changes in energy costs the internal efficiency of
the system with an installed capacity of 400kWp proves
superior.
TABLE V. INTERNAL RATE OF RETURN FOR 500KWP PV
100kWh
200kWh
300kWh
400kWh
0.16 €
32.85%
31.26%
29.81%
28.44%
0.14 €
29.74%
28.26%
26.91%
25.64%
0.12 €
26.57%
25.83%
23.97%
22.79%
TABLE VI. INTERNAL RATE OF RETURN FOR 600KWP PV
100kWh
200kWh
300kWh
400kWh
0.16 €
29.12%
28.10%
27.25%
26.33%
0.14 €
26.29%
25.34%
24.55%
23.69%
0.12 €
23.41%
22.53%
21.80%
21.00%
Despite this, in terms of net present values, the 600kWp
system appears more favorable, although it is closely
competes with the 500kWp system.
TABLE VII. NET PRESENT VALUE FOR 500KWP PV
100kWh
200kWh
300kWh
400kWh
0.16 €
917,811.
81 €
925,788.
27 €
930,984.
96 €
931,555.59
€
0.14 €
799,157.
73 €
803,211.
32 €
804,830.
64 €
802,088.24
€
0.12 €
680,503.
65 €
705,556.
06 €
678,676.
32 €
672,620.90
€
TABLE VIII. NET PRESENT VALUE FOR 600KWP PV
100kWh
200kWh
300kWh
400kWh
0.16 €
918,496.
61 €
932,631.
82 €
949,048.
13 €
956,892.38
€
0.14 €
793,157.
92 €
802,784.
38 €
814,891.
77 €
818,712.39
€
0.12 €
667,819.
23 €
672,936.
93 €
680,735.
40 €
680,532.39
€
Once more, the aforementioned two systems appear
preferable preferable. Summarizing the results based on the
net present value (Fig. 12), the system with an installed PV
capacity of 500kWp and a battery capacity of 200kWh is
selected due to smaller variations in energy cost fluctuations.
Fig. 12. Comparison of 500kWp-600kWp systems.
During the summer months the energy demand of
Heraklion Port can be supplied by 37.5% through the
combination of PV and Battery (Fig. 13).
Fig. 13. Power distribution in the installation.
The optimization in the use of the energy generated by the
battery in the installation has a significant benefit on the costs
imposed on the power demand (Fig. 14), which annually
reaches 28.814,34 €.
Fig. 14. Impact on power costs.
4. Conclusion
The global energy landscape is experiencing a profound
transformation, propelled by the pressing demand for
sustainable solutions. Large medium-voltage consumers,
encompassing diverse structures from high-rise residential to
commercial office spaces and healthcare facilities, represent a
substantial portion of global energy consumption.
Historically,these buildings have heavily depended on the
utility grid to fulfill their energy requirements. However, this
dependency poses a multifaceted challenge - fluctuating
electricity costs can strain budgets and reliance on fossil fuel
production contributes to environmental pollution.
In the case study the benefits to energy costs are notably
significant, amounting to €136,055.41 per year. This
underscores the pivotal role of storage as a critical component
in maximising the utilization of solar energy.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
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Volume 6, 2024
References
[1] International Renewable Energy Agency., Electricity
storage and renewables: costs and markets to 2030.
[Abu Dhabi]: International Renewable Energy Agency,
2017.
[2] International Energy Agency (IEA)., Renewables 2021:
Analysis and forecast to 2026. IEA, 2021.
[3] U.S. Department of Energy., Grid modernization: Key to
an efficient, reliable, resilient, and secure power system.
DOE, 2017.
[4] European Commission, Energy storage: The role of
electricity. European Commission, 2019
[5] M. A. Green, E. D. Dunlop, D. H. Levi, J. Hohl‐Ebinger,
M. Yoshita, and A. W. Y. Ho‐Baillie, ‘Solar cell
efficiency tables (version 54)’, Progress in
Photovoltaics, vol. 27, no. 7, pp. 565–575, July 2019,
doi: 10.1002/pip.3171.
[6] D. Song, H. Jiao, and C. T. Fan, ‘Overview of the
photovoltaic technology status and perspective in
China’, Renewable and Sustainable Energy Reviews,
vol. 48, pp. 848–856, August 2015, doi:
10.1016/j.rser.2015.04.001.
[7] M. M. Rahman, A. O. Oni, E. Gemechu, and A. Kumar,
‘Assessment of energy storage technologies: A review’,
Energy Conversion and Management, vol. 223, p.
113295, November 2020, doi:
10.1016/j.enconman.2020.113295.
[8] H. A. Behabtu et al., ‘A Review of Energy Storage
Technologies’ Application Potentials in Renewable
Energy Sources Grid Integration’, Sustainability, vol. 12,
no. 24, p. 10511, December 2020, doi:
10.3390/su122410511.
[9] R. Haas, and F. Schlögl, ‘Barriers and solutions for the
adoption of renewable energy technologies: Lessons
learned from the innovation systems of Denmark,
Germany and the Netherlands’, Energy Policy, vol. 128,
pp. 847-856, 2019.
[10] J. Yan, M. Yu, and C. Wang, ‘Synergistic integration of
distributed photovoltaic generation and energy storage
system in distribution network operation considering
uncertainties’, IEEE Transactions on Industrial
Informatics, vol. 16, no. 4, pp. 2432-2441, 2020.
[11] W. Lu, M. Shahidehpour, and Q. Zeng, ‘Energy storage
system planning for enhancing the sustainability and
resilience of distribution grids with high photovoltaic
penetration’, IEEE Transactions on Sustainable Energy,
vol. 11, no. 3, pp. 1984-1995, 2020.
International Journal of Electrical Engineering and Computer Science
DOI: 10.37394/232027.2024.6.19
Emmanuel Karapidakis, Marios Nikologiannis,
Marini Markaki, Ioannis Grammatikakis
E-ISSN: 2769-2507
164
Volume 6, 2024
Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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